Senior AI Infrastructure Engineer
Senior AI Infrastructure Engineer
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Job Description
Responsibilities
1. Full-Stack AI Infrastructure Architecture & Development:
- Build a full-stack AI infrastructure system for quantitative scenarios based on Kubernetes unifying the management of heterogeneous computing resources (e.g. GPU pooling).
- Integrate high-performance communication layers (e.g. RDMA) and drive the unified development of AI training/inference platforms and GPU operation/maintenance platforms.
- Streamline the end-to-end workflow from resource scheduling to model deployment enhancing system efficiency and stability.
2. Intelligent Computing Power Scheduling System Design:
- Design a global scheduling mechanism supporting multi-task types and priority strategies leveraging Volcano scheduler capabilities.
- Lead the customization and maintenance of Volcano and core Operators optimizing elastic scaling and resource utilization based on dynamic demands of quantitative tasks.
3. Hardware-Software Co-Optimization & System Reliability:
- Develop an intermediate layer bridging underlying hardware (GPU/networking/storage) and AI frameworks (PyTorch/TensorFlow).
- Build GPU elastic resource pools fault self-healing mechanisms and unified observability platforms (e.g. monitoring dashboards).
- Ensure high-efficiency iteration and high availability of large-scale model training through performance tuning and automated operations.
4. Technical Foresight & Architecture Evolution:
- Drive long-term AI Infra roadmap planning anticipating quantitative business needs in computing scale training efficiency and cost control.
- Explore and validate cutting-edge architectures (e.g. heterogeneous computing fusion compute-storage separation Serverless AI) to enhance infrastructure capabilities and technical barriers.
Qualifications
1. Bachelors/Masters in Computer Science or related fields 5-10 years of experience with strong self-motivation and execution ability to identify and resolve technical bottlenecks.
2. Deep expertise in AI infrastructure: Kubernetes GPU resource management RDMA/high-performance networking and large-scale distributed AI system design/deployment.
3. Proficient in *Golang/Python* with solid system programming and automation skills. Priority given to candidates with experience in *Volcano/Kueue schedulers K8s Operator development or open-source contributions*.
4. Familiar with core resource scheduling principles GPU lifecycle management (allocation isolation elasticity fault tolerance) and designing high-availability low-latency strategies for quantitative tasks.
5. Knowledge of mainstream AI frameworks (PyTorch/TensorFlow) with experience in training/inference performance optimization and cross-team collaboration for framework-infra co-optimization.
6. Preferred: Experience in **FinTech/quantitative AI infrastructure* understanding of business-critical computing demands and ability to drive cross-team collaboration and value delivery.
Employment Type
Full Time
